Whole-Slide Tumor Segmentation for Reproducible Necrosis Ratio Analysis
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Solution Overview
Problem
Existing methods for assessing tumor necrosis ratio in osteosarcoma are subjective and prone to intraobserver and interobserver variability, making it challenging to reliably predict patient outcomes.
Innovation Solution
A deep learning-based approach using the Deep Multi-Magnification Network for pixel-wise segmentation of viable and necrotic tumor regions in whole slide images, enabling objective and reproducible estimation of necrosis ratio, which is then correlated with patient outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to evaluate tumor necrosis ratio, then clinical judgment and experience can be applied, but intraobserver and interobserver variability increases and measurement precision decreases
Solution Approach 1:
The patent replaces manual visual assessment (mechanical/human system) with an automated image processing system that uses color deconvolution and machine learning algorithms to objectively quantify necrosis ratio, eliminating human variability while maintaining measurement precision
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a bridge between raw histology images and clinical decision-making, using standardized algorithms to transform subjective visual assessment into objective, reproducible quantitative metrics
2Measurement precision
If automated image processing is applied to segment tumor regions, then measurement precision and reproducibility improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex task of tumor assessment into distinct segments: color deconvolution to separate staining components, thresholding to identify necrotic regions, and ratio calculation to quantify results. This modular approach improves precision while managing system complexity through structured processing steps
Solution Approach 2:
The patent transforms the assessment from qualitative visual parameters to quantitative color space parameters (optical density values in separated staining channels), enabling precise automated measurement while using standard image processing techniques that don't require overly complex systems
3Measurement precision
If pixel-level segmentation is performed to accurately distinguish viable and necrotic tumor regions, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary color deconvolution to separate hematoxylin and eosin staining components before segmentation, pre-processing the image to make subsequent thresholding and region identification more efficient and accurate, reducing overall processing time despite detailed pixel-level analysis
Solution Approach 2:
The patent changes from analyzing raw intensity values to analyzing optical density values in separated color channels, which enhances contrast between viable and necrotic regions, allowing faster and more accurate segmentation with reduced computational burden
Data Source
AI summary
Presented herein are systems and methods of determining predicted outcomes of subjects with cancer from biomedical images. A computing system may identify a biomedical image of a tissue sample from a subject with cancer. The biomedical image may have (i) a first region of interest (ROI) corresponding to viable tumor and (ii) a second ROI corresponding to necrotic tumor in the tissue sample. The computing system may apply a machine learning model to the biomedical image to determine (i) a first segment identifying the first ROI and (ii) a second segment identifying the second ROI. The computing system may determine a ratio between a first size of the first segment associated with the viable tumor and a second size of the second segment associated with the necrotic tumor. The computing system may generate a value indicative of a predicted outcome of the cancer in the subject using the ratio.